This application claims the priority of German Application No. 100 35 505.6, filed Jul. 21, 2000, the disclosure of which is expressly incorporated by reference herein.
The invention relates to a method for recognizing the severity of a vehicle collision, where the output signal of one or more acceleration sensors is processed and fed to a neural network that controls a release unit for an occupant protection device. Several occupant protection devices can be selected by the release unit in accordance with the severity and course of the vehicle collision.
Such a method is known as disclosed in German Patent document DE 198 54 380 A. There, an output signal of several acceleration sensors is processed and entered into the neural network in order to obtain a statement about the severity of a vehicle collision.
The invention is based on the problem of trying to create a method of the above-described kind that offers a fast and meaningful opportunity for predicting the severity of a vehicle collision.
The invention solves this problem by providing a method for recognizing the severity of a vehicle collision, where the output signal of one or more acceleration sensors is processed and fed to a neural network that controls a release unit for an occupant protection device. Several occupant protection devices can be selected by the release unit in accordance with the severity and course of the vehicle collision. The future time progression of the output signal of the acceleration sensor is predicted with the help of the neural network based on the acceleration sensor signal values during at least one defined point in time.
Input variables into the neural network are the processed signals of preferably several acceleration sensors that are distributed throughout the vehicle. Processing means looking at simple or multiple integrated or otherwise filtered sensor signals and using either the time series as such as input variables or determining special, characteristic values from the signal progression line and feeding them as input to the neural network. These characteristic variables can include: values of the processed signals at certain defined times with regard to the time of the evaluation, signal values at special trigger times, achieved maximum values, time periods for certain signal increases, increases in the signal progression line, arithmetic combinations of various processed signals, or the like.
The output variable of the neural network is a statement about the crash severity and thus about the expected driver and passenger impact. This statement can be particularly close to reality due to the statement of the future course of the acceleration signal value(s) obtained through the neural network. By contrast, the statement about the crash severity is usually directly connected with the actuator system on existing air bag trigger algorithms. With the increasing number of restraint systems, a formulation of the crash severity would become increasingly complicated. The present invention provides a parametric definition of a crash severity that is independent from the respective actuator system that is employed.
The crash severity in the case of a head-on collision is therefore defined by the expected course of the acceleration sensor signal. This progression line is identical to the expected acceleration of the occupant compartment.
With the help of the neural network, the subsequent time line of the signal is calculated. The result of this calculation is a statement about the expected forward movement of the unrestrained occupant. This results in statements such as, for example: without restraint the occupant would bounce against the steering wheel at approximately 10 m/s in 50 ms (corresponds to a forward movement of about 300 mm).
This statement is then counteracted by an appropriate action of the occupant protection and restraint devices, which are controlled by the releasing unit.
Consideration of the free movement of the vehicle occupant in connection with the selection of occupant protection and restraint devices is basically known from French Patent document FR 21 84 307 and European Patent document EP 327 853 B. An air bag system is released when the expected forward movement of the vehicle occupant exceeds a certain threshold value (FR 21 84 307) or when this threshold value is exceeded at two subsequent time intervals (EP 327 853 B). In neither case, the future acceleration behavior of the vehicle occupant is deduced, only a distinction is made between xe2x80x9cFIRE/NO-FIRE.xe2x80x9d By contrast, in the case of the present invention, a statement is obtained with the help of the neural network about the expected impact of the vehicle occupant, and from this the possibility is deduced how this impact can be counteracted by an appropriate release strategy of the occupant protection and restraint devices in a specific manner.
The processing in accordance with the invention of the acceleration signals with the help of the neural network and the prediction of the expected impact offers a generalized statement (hereinafter called crash severity parameter (CSP) because in this statement about the crash severity neither vehicle type and equipment nor load drop/barrier are considered. Although the occurring values of the CSP are dependent upon the vehicle structure, the effect on the vehicle occupant always only depends on the CSP value itself.
The CSP now is directly related to the occupant impact. A deduction from the crash position/speed/barrier stiffness or similar on the occupant impact is no longer required.
With the CSP that is determined in accordance with the invention, it is also possible to differentiate between accident situations where different release strategies of the occupant protection and restraint devices are supposed to occur with sufficient exactness. The CSP is therefore not dependent upon the occupant restraint devices. This makes it possible to adjust it optimally in its effectiveness.